A small-area analysis of inequalities in chronic disease prevalence across urban and non-urban communities in the Province of Nova Scotia, Canada, 2007–2011
Bibliographic record
Abstract
BACKGROUND: Small-area studies of health inequalities often have an urban focus, and may be limited in their translatability to non-urban settings. Using small-area units representing communities, this study assessed the influence of living in different settlement types (urban, town and rural) on the prevalence of four chronic diseases (heart disease, cancer, diabetes and stroke) and compared the degrees of associations with individual-level and community-level factors among the settlement types. METHODS: The associations between community-level and individual-level characteristics and prevalence of the chronic diseases were assessed using logistic regression (multilevel and non-multilevel) models. Individual-level data were extracted from the Canadian Community Health Survey (2007-2011). Indices of material deprivation and social isolation and the settlement type classification were created using the Canadian Census. RESULTS: Respondents living in towns were 21% more likely to report one of the diseases than respondents living in urban communities even after accounting for individual-level and community-level characteristics. Having dependent children appeared to have protective effects in towns, especially for males (OR: 0.49 (95% CI 0.27 to 0.90)). Unemployment had a strong association for all types of communities, but being unemployed appeared to be particularly damaging to health of males in urban communities (OR: 2.48 (95% CI 1.43 to 4.30)). CONCLUSIONS: The study showed that those living in non-urban settings, particularly towns, experience extra challenges in maintaining health above and beyond the socioeconomic condition and social isolation of the communities, and individual demographic, behavioural and socioeconomic attributes. Our findings also suggest that health inequality studies based on urban-only settings may underestimate the risks by some factors. Ways to devise meaningful small-area units comparable in all settlement types are necessary to help plan effective provision of chronic disease-related health services and programmes on a regional scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".